Evaluation metric: the evaluation for VQA is different from image classification. The accuracy for a single answer could be a number between 0 and 1 [1]}:
\(\text{Acc}(ans) = \min \lbrace {(\# \text{human that said } ans)}/{3},1\rbrace .\) 
We generalize the uncertainty evaluation accordingly:
\(\leavevmode \xbox {resizebox}{\XMLaddatt {width}{0.0pt}n_{ac} = \sum _{i} \text{Acc}_i \text{Cer}_i,~n_{iu} = \sum _{i} (1-\text{Acc}_i)(1- \text{Cer}_i)\,}, \leavevmode \xbox {resizebox}{\XMLaddatt {width}{0.0pt}n_{au} = \sum _{i} \text{Acc}_i (1-\text{Cer}_i),~n_{ic} = \sum _{i} (1-\text{Acc}_i)(\text{Cer}_i)\,}\) 
